The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Recent advancements in the field of image processing and computer vision have led to development of various techniques for tracking moving objects in a scene. A sizeable number of algorithms are based on foreground extraction techniques to extract foreground from a scene that comprises a foreground and a background. Direct foreground extraction techniques such as frame differencing, mean/median filter differencing, Gaussian mixture model based foreground extraction etc. are susceptible to errors due to the dynamicity in the background of the scene. For example, atmospheric turbulence distortions may lead to false classification of object in the image. Algorithms such as SubSENSE, ViBe, and Pixel based Adaptive Segmenter (PBAS) are based on background estimation techniques. Though such techniques are better than foreground estimation techniques, the background estimation techniques are also susceptible to distortions like atmospheric turbulence and dynamicity in the image background and particularly fail to reliably detect faint and small moving objects. Moreover, the background estimation techniques are computationally expensive which makes them unsuitable for real time implementation.
The existing techniques for detection of moving objects are generally based on preservation of moving object of the scene along with elimination of distortions caused due to a variety of reasons such as dynamically varying background. One such technique comprises computation of a reference background image by use of a variation median filter. A displacement map of the individual frames of the reference background image may be created and segmented based on sparse optical flow. In such a technique, though the moving objects are preserved, residual turbulent motion is not completely eliminated. Further, it has been experimentally determined that techniques based on optical flow are unreliable in case of turbulence in the scene. Additionally, the aforementioned technique is computationally expensive in scenario when the moving object occupies a significant portion of the scene. Certain other techniques have also been developed that are based on a variant of robust principal component analysis. Such techniques are focused on recovery of a stable background of the scene along with moving objects present in the scene. Such techniques comprise weighting based on optical flow for separation of moving objects from the turbulence present in the scene. Consequently, the moving objects are reliably tracked when the levels of turbulence in the scene is decreased by frame averaging. However, in scenarios where the motion of the moving object is fast, such technique proves to be computationally expensive due to high memory requirements. Further, such a technique fails to reliably track the moving object due to excessive dependence on the optical flow.
In light of the aforementioned drawbacks associated with the existing techniques, there exists a need for a method that robustly tracks the moving object while eliminating distortions. It is also desired that such a technique is not affected by the change in the properties of the potential moving object that are to be tracked. Further, it is desirable that implementation of such a technique is computationally efficient in real time tracking of moving objects.